WCDMA signals experience interference in dispersive channels. This interference is a combination of self-interference, such as intersymbol interference; multiple access interference, i.e., interference due to non-zero code cross-correlation; interference from other cells in the downlink; or interference from other users in the uplink. This interference must be suppressed in order to achieve good throughput, in particular for High Speed Packet Access (HSPA) receivers. In addition, the enhanced throughput requirements set by 3GPP standardization for type 2 (single antenna terminal) and type 3 (dual antenna terminal) receivers cannot be met without interference suppression.
Linear methods for suppressing interference generally fall into the categories of chip level or symbol level equalization. Symbol level equalization follows the traditional Rake receiver architecture where the received chip-level data is despread at multiple delays, and then the multiple signal images are combined. Chip level equalization reverses the order of these operations. The received chip data is first combined using a linear filter and then despread at a single delay. These techniques are equivalent from a performance perspective, and the present invention applies to either equalization approach.
For symbol level equalization, one effective approach is the Generalized Rake receiver, or G-Rake. A G-Rake receiver calculates combining weights to perform both coherent combining of symbols despread at different delay values, as well as interference suppression by accounting for interference temporal and spatial correlations in the combining weight formulation. The combining weights are given byw=Ru−1hwhere Ru is an impairment covariance matrix and h is a vector of net channel coefficients. Here, the term “impairment” includes both interference and noise, while the term “net channel coefficient” refers to a channel coefficient that includes the effects of the transmit and receive filters as well as the fading wireless channel.
There are two general approaches to obtaining the impairment covariance matrix Ru in a G-Rake receiver—nonparametric and parametric. Nonparametric method(s) are blind, and estimate Ru, directly from observed data. The parametric method assumes an underlying model, and computes Ru from model parameters.
For chip equalization, the received signal at the chip level is given byr=Hc+v where r is a block of received chips, H is a convolution matrix of chip or sub-chip spaced versions of the net channel coefficients, v represents white Gaussian noise due to neighboring base stations and thermal noise, and c is the transmitted chip sequence. The chip equalizer filter f that suppresses the interference in (2) is the solution tof=A−1b,where A is a correlation matrix of received pilot chips, and b is a cross-correlation vector of received pilot chips with actual pilot chips.
Similar to G-Rake, there are two ways to generate the chip equalizer filter—a nonparametric form and a parametric form. These two forms differ primarily in how the A matrix is calculated. The nonparametric form uses the received chip data directly to calculate the A matrix. The parametric form works instead with the channel impulse response and the powers of the serving base station and the white Gaussian noise.
The existing parametric and nonparametric equalization approaches have different strengths and weaknesses. These are discussed with respect to the G-Rake receiver. The same strengths and weaknesses generally hold for chip equalization as well.
The strength of the parametric G-Rake approach is that performance (measured, e.g., by BER, BLER, or throughput) is relatively insensitive to the speed of the mobile receiver, such as a WCDMA user equipment (UE). The main weakness of the parametric approach is that it relies on channel information developed by the path searcher/delay estimator in the receiver. If this information is incorrect, then the effective color of the impairment will be incorrectly modeled. This mis-modeling degrades the performance of the G-Rake receiver.
The strength of the nonparametric approach is that it is a blind technique. There is no specific model for interference, so all interference is captured by the estimation approach. This blind approach is also indirectly a weakness. Blind approaches typically need a significant amount of “training” data to perform well. In a WCDMA system, the pilot channel has only 10 symbols per slot, so the pilot-based approach to covariance estimation requires significant smoothing (filtering) to work well. Smoothing limits the effectiveness of the approach to low UE speed.
Receivers employing either parametric or nonparametric techniques for channel equalization and interference suppression are thus optimal only under the circumstances for which the respective method generates the best results, and are suboptimal under other circumstances.